The Semantic Web is a set of technologies, which aim to make the content of the resources of the Web accessible and usable by machines (in particular thanks to programs and software agents) through a system of data and metadata.
RDF (Resource Description Framework) is the language that can be used for resource representation in a semantically rich way. RDF is a data model of semantic graph (a collection of RDF statements can intrinsically represent a graph of linked “concepts”), designed for helping to describe the resources on the Web and their metadata, allowing automated processing. It is implemented using a variety of syntax formats, but XML is generally preferred.
The mentioned items of a semantic graph named “concepts” (or sometimes “entities”) are to be understood as a basic unit of computationally represented meaning, usually embodying a topic of interest. A concept has a representation intended for human consumption (sometimes a plurality of representations, when the same concept is known under more than one name), and an identifier (called a URI, Uniform Resource Identifier) for identification by machines. The concept meaning is further specified by possible properties, that may have literal values or point to another concept (examples of concepts will be presented below). Properties that point to other concepts create the links between concepts that constitute the semantic graph.
Thanks to initiatives like Linked Open Data, a practice of publishing structured data on the Web built upon compatible standard Web technologies and interlinking it has emerged, enabling the emergence of a giant rapidly-growing semantic graph of linked concepts describing general knowledge as particular areas (e.g. geography, humanities, etc.) Most notably, databases such as DBpedia or Freebase contain millions of concepts from general encyclopedic knowledge, and are still exploring the internet to gather new concepts.
Such ability to automatically process the human knowledge by machines offers new possibilities for many usage scenarios and especially for helping solving various problems, e.g. innovation problems. From the description of a given problem, the idea is to explore the semantic graph in search of concepts related to potential solutions. In the millions of existing concepts known to humanity, finding those that are the most likely to point to a solution remains very difficult.
In the context of semantic annotation, some known “concept recommendation systems” enable, from a sample text that describes the problem to identify concepts directly relevant to the problem (the “initial” concepts) and to place them on the semantic graph, in order to help the user to discover concepts which are in the vicinity of some initial concepts and to deepen the understanding of the current domain of interest of the user.
Similar concept recommendation is for example performed by search engines like Google that suggest and display search queries (based on other users' search activities), which might be similar to the one a user is typing in the search bar.
However, it appears that these systems offer only a partial view of the conceptual space that is driven mostly by obvious associations with initial concepts, and so they still do not provide sufficiently valuable results, enriching for the user.
There is a need for new methods of smart exploration of semantic graphs to enable the user to discover less obvious useful concepts.